path: root/lab-windows/dsss_experiments-ber.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "import struct\n",
    "import random\n",
    "import itertools\n",
    "import datetime\n",
    "import multiprocessing\n",
    "from collections import defaultdict\n",
    "import json\n",
    "import traceback\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib import ticker\n",
    "import numpy as np\n",
    "from scipy import signal as sig\n",
    "from scipy import fftpack as fftpack\n",
    "import ipywidgets\n",
    "from IPython.display import set_matplotlib_formats\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "import colorednoise\n",
    "\n",
    "np.set_printoptions(linewidth=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib widget\n",
    "%matplotlib inline\n",
    "set_matplotlib_formats('png', 'pdf')\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 10}\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_rate = 10 # sp/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py\n",
    "preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],\n",
    "                        8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]], \n",
    "                        10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}\n",
    "\n",
    "def gen_gold(seq1, seq2):\n",
    "    gold = [seq1, seq2]\n",
    "    for shift in range(len(seq1)):\n",
    "        gold.append(seq1 ^ np.roll(seq2, -shift))\n",
    "    return gold\n",
    "\n",
    "def gold(n):\n",
    "    n = int(n)\n",
    "    if not n in preferred_pairs:\n",
    "        raise KeyError('preferred pairs for %s bits unknown' % str(n))\n",
    "    t0, t1 = preferred_pairs[n]\n",
    "    (seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)\n",
    "    return gen_gold(seq0, seq1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modulate(data, nbits=5, pad=True):\n",
    "    # 0, 1 -> -1, 1\n",
    "    mask = np.array(gold(nbits))*2 - 1\n",
    "    \n",
    "    sel = mask[data>>1]\n",
    "    data_lsb_centered = ((data&1)*2 - 1)\n",
    "\n",
    "    signal = (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2\n",
    "    if pad:\n",
    "        return np.hstack([ np.zeros(len(mask)), signal, np.zeros(len(mask)) ])\n",
    "    else:\n",
    "        return signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x):\n",
    "    mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
    "\n",
    "    sequence -= np.mean(sequence)\n",
    "    \n",
    "    return np.array([np.correlate(sequence, row, mode='full') for row in mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 50.00341 len: 1227133\n"
     ]
    }
   ],
   "source": [
    "with open('data/fmeas_export_ocxo_2day.bin', 'rb') as f:\n",
    "    meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
    "    print('mean:', np.mean(meas_data), 'len:', len(meas_data))\n",
    "    meas_data -= np.mean(meas_data)\n",
    "    def mains_noise(n):\n",
    "        last_valid = len(meas_data) - n\n",
    "        start = np.random.randint(last_valid)\n",
    "        return meas_data[start:start+n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_test_signal(duration, nbits=6, signal_amplitude=2.0e-3, decimation=10, seed=0, data=None):\n",
    "    test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), duration) if data is None else data\n",
    "    \n",
    "    signal = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
    "    noise = mains_noise(len(signal))\n",
    "    \n",
    "    return test_data, signal + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_export_clean():\n",
    "    test_duration = 200\n",
    "    test_nbits = 5\n",
    "    test_signal_amplitude=2.0e-3\n",
    "    test_decimation=10\n",
    "\n",
    "    for test_signal_amplitude in [2.0e-3, 20e-3, 200e-3, 2]:\n",
    "        test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**test_nbits), test_duration)\n",
    "        #test_data = np.array([0, 1, 2, 3] * 50)\n",
    "        signal = np.repeat(modulate(test_data, test_nbits, pad=False) * 2.0 - 1, test_decimation) * test_signal_amplitude\n",
    "        with open(f'dsss_test_signals/dsss_test_noiseless_{test_signal_amplitude*1000:.0f}mHz.bin', 'wb') as f:\n",
    "            for e in signal:\n",
    "                f.write(struct.pack('<f', e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig, ax = plt.subplots()\n",
    "#ax.psd(colorednoise.powerlaw_psd_gaussian(1, 1000))\n",
    "#None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_export_noisy():\n",
    "    test_duration = 32\n",
    "    test_nbits = 5\n",
    "    test_signal_amplitude=2.0e-3\n",
    "    test_decimation=10\n",
    "    test_signal_amplitude = 200e-3\n",
    "    noise_level = 10e-3\n",
    "\n",
    "    #test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**test_nbits), test_duration)\n",
    "    #test_data = np.array([0, 1, 2, 3] * 50)\n",
    "    test_data = np.array(range(test_duration))\n",
    "    signal = np.repeat(modulate(test_data, test_nbits, pad=False) * 2.0 - 1, test_decimation) * test_signal_amplitude\n",
    "    noise = colorednoise.powerlaw_psd_gaussian(1, len(signal)*10) * noise_level\n",
    "    noise[-int(1.5*len(signal)):][:len(signal)] += signal\n",
    "\n",
    "    with open(f'dsss_test_signals/dsss_test_noisy_padded.bin', 'wb') as f:\n",
    "        for e in noise:\n",
    "            f.write(struct.pack('<f', e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2 * (np.clip(np.abs(x), 0, 0.5) * 2)**5\n",
    "\n",
    "def plot_distance_func():\n",
    "    fig, ax = plt.subplots()\n",
    "    x = np.linspace(-1.5, 5.5, 10000)\n",
    "    ax.plot(x, nonlinear_distance(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distance_func()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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iM7LKdh/U4q5648CyM4trr1ktabkzi5e4ddM9+M4sLr2L13o7ssRrQxV1PpDF1dAQrX4SCyrFPC3IZCcW1zUFC9NL6p1YZEczXNefO7LovSayOfOLWxbZcYbcP5HleD26vZx6zf749ePz67t4uZT+45cbztdHH/4liw/+3gSjfui3L7557bcv9Px//yscx9Pvh/nY6J8//g22t+n/CmVuZHN0cmVhbQplbmRvYmoKMTEgMCBvYmoKMzQ1MQplbmRvYmoKMTYgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzOTIgPj4Kc3RyZWFtCnicPVJLbgUxCNvPKbhApfBNcp6p3u7df1ubzFSqCi8DtjGUlwypJT/qkogzTH71cl3iUfK9bGpn5iHuLjam+FhyX7qG2HLRmmKxTxzJL8i0VFihVt2jQ/GFKBMPAC3ggQXhvhz/8ReowdewhXLDe2QCYErUbkDGQ9EZSFlBEWH7kRXopFCvbOHvKCBX1KyFoXRiiA2WACm+qw2JmKjZoIeElZKqHdLxjKTwW8FdiWFQW1vbBHhm0BDZ3pGNETPt0RlxWRFrPz3po1EytVEZD01nfPHdMlLz0RXopNLI3cpDZ89CJ2Ak5kmY53Aj4Z7bQQsx9HGvlk9s95gpVpHwBTvKAQO9/d6Sjc974CyMXNvsTCfw0WmnHBOtvh5i/YM/bEubXMcrh0UUqLwoCH7XQRNxfFjF92SjRHe0AdYjE9VoJRAMEsLO7TDyeMZ52d4VtOb0RGijRB7UjhE9KLLF5ZwVsKf8rM2xHJ4PJntvtI+UzMyohBXUdnqots9jHdR3nvv6/AEuAKEZCmVuZHN0cmVhbQplbmRvYmoKMTcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA5MCA+PgpzdHJlYW0KeJxNjUESwCAIA++8Ik9QRND/dHrS/1+r1A69wE4CiRZFgvQ1aksw7rgyFWtQKZiUl8BVMFwL2u6iyv4ySUydhtN7twODsvFxg9JJ+/ZxegCr/XoG3Q/SHCJYCmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzcKL1N1YnR5cGUgL0Zvcm0gL1R5cGUgL1hPYmplY3QgPj4Kc3RyZWFtCnic4zI0MFMwNjVVyOUyNzYCs3LALCNzIyALJItgQWTTAAFfCgoKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDgwID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4mZp8olbN/GyBK3HBPunu4OhIyU95hhocEngwshlPxBpmjYDW4RlKNneyjsG5fdYHmelOr9fcHKk92dnE9zcsZ9AplbmRzdHJlYW0KZW5kb2JqCjIwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNDkgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhysNAMboDSYKZW5kc3RyZWFtCmVuZG9iagoyMSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMxNyA+PgpzdHJlYW0KeJw1UktyQzEI279TcIHOmL99nnSyau6/rYQnK7AtQEIuL1nSS37UJdulw+RXH/clsUI+j+2azFLF9xazFM8tr0fPEbctCgRREz34MicVItTP1Og6eGGXPgOvEE4pFngHkwAGr+FfeJROg8A7GzLeEZORGhAkwZpLi01IlD1J/Cvl9aSVNHR+Jitz+XtyqRRqo8kIFSBYudgHpCspHiQTPYlIsnK9N1aI3pBXksdnJSYZEN0msU20wOPclbSEmZhCBeZYgNV0s7r6HExY47CE8SphFtWDTZ41qYRmtI5jZMN498JMiYWGwxJQm32VCaqXj9PcCSOmR0127cKyWzbvIUSj+TMslMHHKCQBh05jJArSsIARgTm9sIq95gs5FsCIZZ2aLAxtaCW7eo6FwNCcs6Vhxtee1/P+B0Vbe6MKZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMzOCA+PgpzdHJlYW0KeJw1Ujmu3UAM630KXSCAds2c5wWpfu7fhpRfCkO0VoqajhaVafllIVUtky6/7UltiRvy98kKiROSVyXapQyRUPk8hVS/Z8u8vtacESBLlQqTk5LHJQv+DJfeLhznY2s/jyN3PXpgVYyEEgHLFBOja1k6u8Oajfw8pgE/4hFyrli3HGMVSA26cdoV70PzecgaIGaYlooKXVaJFn5B8aBHrX33WFRYINHtHElwjI1QkYB2gdpIDDmzFruoL/pZlJgJdO2LIu6iwBJJzJxiXTr6Dz50LKi/NuPLr45K+kgra0zad6NJacwik66XRW83b309uEDzLsp/Xs0gQVPWKGl80KqdYyiaGWWFdxyaDDTHHIfMEzyHMxKU9H0ofl9LJrookT8ODaF/Xx6jjJwGbwFz0Z+2igMX8dlhrxxghdLFmuR9QCoTemD6/9f4ef78Axy2gFQKZW5kc3RyZWFtCmVuZG9iagoyMyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI0OCA+PgpzdHJlYW0KeJwtUTmSA0EIy+cVekJz0++xy5H3/+kKygGDhkMgOi1xUMZPEJYr3vLIVbTh75kYwXfBod/KdRsWORAVSNIYVE2oXbwevQd2HGYC86Q1LIMZ6wM/Ywo3enF4TMbZ7XUZNQR712tPZlAyKxdxycQFU3XYyJnDT6aMC+1czw3IuRHWZRikm5XGjIQjTSFSSKHqJqkzQZAEo6tRo40cxX7pyyOdYVUjagz7XEvb13MTzho0OxarPDmlR1ecy8nFCysH/bzNwEVUGqs8EBJwv9tD/Zzs5Dfe0rmzxfT4XnOyvDAVWPHmtRuQTbX4Ny/i+D3j6/n8A6ilWxYKZW5kc3RyZWFtCmVuZG9iagoyNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxMCA+PgpzdHJlYW0KeJw1UMsNQzEIu2cKFqgUAoFknla9df9rbdA7YRH/QljIlAh5qcnOKelLPjpMD7Yuv7EiC611JezKmiCeK++hmbKx0djiYHAaJl6AFjdg6GmNGjV04YKmLpVCgcUl8Jl8dXvovk8ZeGoZcnYEEUPJYAlquhZNWLQ8n5BOAeL/fsPuLeShkvPKnhv5G5zt8DuzbuEnanYi0XIVMtSzNMcYCBNFHjx5RaZw4rPWd9U0EtRmC06WAa5OP4wOAGAiXlmA7K5EOUvSjqWfb7zH9w9AAFO0CmVuZHN0cmVhbQplbmRvYmoKMTQgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9DaGFyUHJvY3MgMTUgMCBSCi9FbmNvZGluZyA8PAovRGlmZmVyZW5jZXMgWyA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgNTQgL3NpeCA1NiAvZWlnaHQgXQovVHlwZSAvRW5jb2RpbmcgPj4KL0ZpcnN0Q2hhciAwIC9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnREZXNjcmlwdG9yIDEzIDAgUgovRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXSAvTGFzdENoYXIgMjU1IC9OYW1lIC9EZWphVnVTYW5zCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDEyIDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnROYW1lIC9EZWphVnVTYW5zIC9JdGFsaWNBbmdsZSAwCi9NYXhXaWR0aCAxMzQyIC9TdGVtViAwIC9UeXBlIC9Gb250RGVzY3JpcHRvciAvWEhlaWdodCAwID4+CmVuZG9iagoxMiAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNSAwIG9iago8PCAvZWlnaHQgMTYgMCBSIC9mb3VyIDE3IDAgUiAvb25lIDE5IDAgUiAvcGVyaW9kIDIwIDAgUiAvc2l4IDIxIDAgUgovdGhyZWUgMjIgMCBSIC90d28gMjMgMCBSIC96ZXJvIDI0IDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTQgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvQ0EgMCAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMiA8PCAvQ0EgMSAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9EZWphVnVTYW5zLW1pbnVzIDE4IDAgUiA+PgplbmRvYmoKMiAwIG9iago8PCAvQ291bnQgMSAvS2lkcyBbIDEwIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKMjUgMCBvYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDIwMDQwODE2MzkxNyswMicwMCcpCi9DcmVhdG9yIChtYXRwbG90bGliIDMuMS4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcpCi9Qcm9kdWNlciAobWF0cGxvdGxpYiBwZGYgYmFja2VuZCAzLjEuMykgPj4KZW5kb2JqCnhyZWYKMCAyNgowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAwODM3MyAwMDAwMCBuIAowMDAwMDA4MTU0IDAwMDAwIG4gCjAwMDAwMDgxODYgMDAwMDAgbiAKMDAwMDAwODI4NSAwMDAwMCBuIAowMDAwMDA4MzA2IDAwMDAwIG4gCjAwMDAwMDgzMjcgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzk4IDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMzkyNCAwMDAwMCBuIAowMDAwMDA2OTc0IDAwMDAwIG4gCjAwMDAwMDY3NzQgMDAwMDAgbiAKMDAwMDAwNjQyMCAwMDAwMCBuIAowMDAwMDA4MDI3IDAwMDAwIG4gCjAwMDAwMDM5NDUgMDAwMDAgbiAKMDAwMDAwNDQxMCAwMDAwMCBuIAowMDAwMDA0NTcyIDAwMDAwIG4gCjAwMDAwMDQ3NDIgMDAwMDAgbiAKMDAwMDAwNDg5NCAwMDAwMCBuIAowMDAwMDA1MDE1IDAwMDAwIG4gCjAwMDAwMDU0MDUgMDAwMDAgbiAKMDAwMDAwNTgxNiAwMDAwMCBuIAowMDAwMDA2MTM3IDAwMDAwIG4gCjAwMDAwMDg0MzMgMDAwMDAgbiAKdHJhaWxlcgo8PCAvSW5mbyAyNSAwIFIgL1Jvb3QgMSAwIFIgL1NpemUgMjYgPj4Kc3RhcnR4cmVmCjg1ODcKJSVFT0YK\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#nonlinear_distance_impl = lambda x: np.exp(-np.abs(x)/10) * x**4\n",
    "nonlinear_distance_impl = lambda x: np.exp(-((x/10 - 0.5)%1 - 0.5)**2 / (2*1.2/10**2))\n",
    "\n",
    "def plot_distance_func_impl():\n",
    "    fig, ax = plt.subplots()\n",
    "    x = np.linspace(-30, 30, 10000)\n",
    "    ax.plot(x, nonlinear_distance_impl(x))\n",
    "\n",
    "plot_distance_func_impl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "noprint = lambda *args, **kwargs: None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ricker wavelet, w=69 a=7.3')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "application/pdf": 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+PgpzdHJlYW0KeJw9zLsVgDAIBdA+U7wRQnyA7OOx0v1bwUQbuHzVAx0hGdQNbh2HtKxLd5N96nq1iaTIgNJTalwaToyoaX2pfWrguxvmS9WJP83P5wOHxxlrCmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA0NSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKeBgCffQy1CmVuZHN0cmVhbQplbmRvYmoKMjUgMCBvYmoKPDwgL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzcKL1N1YnR5cGUgL0Zvcm0gL1R5cGUgL1hPYmplY3QgPj4Kc3RyZWFtCnic4zI0MFMwNjVVyOUyNzYCs3LALCNzIyALJItgQWTTAAFfCgoKZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMyMCA+PgpzdHJlYW0KeJw1UbtxxTAM6zUFF/Cd+JU0j3Ovytu/DUA7FWEaBECqvGRKuVzqklWywuRHh+oUTfk+YKb8DvWQ4+ge2SG6U9aWexgIy8Q8pY5YTZZ7uAWBLwxNibmF8/cI6CsGozATgbrF3z9AsyQwaXDwU5BrrVpiiQ48LBZYsyvMrRopVMhVfDs2uQcFcnGz0KccmhS33ILwZYhkR2qxr8tlKfK79QkYhBXmiE8UiYXngQ5mIvEnA2J79tliV1cvqhEZ1kmHB1IE0mxuEjA0RbLqgxvYV8c1P09H2cHJQb+Kwfg2OJkvSXlfBaEQjxf+Ds/ZyLGSQyQU8n21wIgjbIARoU/tIxBlIDRF9+6ZUj4mVYrvAEYhHH2qVzK8F5HZaobN/xld2SoKBlVZH59GcCaDSTjzZKMK01K107/73OPzB2NjeoAKZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDgwID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4mZp8olbN/GyBK3HBPunu4OhIyU95hhocEngwshlPxBpmjYDW4RlKNneyjsG5fdYHmelOr9fcHKk92dnE9zcsZ9AplbmRzdHJlYW0KZW5kb2JqCjI4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNDkgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhysNAMboDSYKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE1NyA+PgpzdHJlYW0KeJxFkLkRQzEIRHNVQQkSsAjqscfRd/+pF/lKtG8ALYevJVOqHyciptzXaPQweQ6fTSVWLNgmtpMachsWQUoxmHhOMaujt6GZh9TruKiquHVmldNpy8rFf/NoVzOTPcI16ifwTej4nzy0qehboK8LlH1AtTidSVAxfa9igaOcdn8inBjgPhlHmSkjcWJuCuz3GQBmvle4xuMF3QE3eQplbmRzdHJlYW0KZW5kb2JqCjMwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNjggPj4Kc3RyZWFtCnicMzM2UzBQsDACEqamhgrmRpYKKYZcQD6IlcsFE8sBs8wszIEsIwuQlhwuQwtjMG1ibKRgZmIGZFkgMSC60gBy+BKRCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMTcgPj4Kc3RyZWFtCnicNVJLckMxCNu/U3CBzpi/fZ50smruv62EJyuwLUBCLi9Z0kt+1CXbpcPkVx/3JbFCPo/tmsxSxfcWsxTPLa9HzxG3LQoEURM9+DInFSLUz9ToOnhhlz4DrxBOKRZ4B5MABq/hX3iUToPAOxsy3hGTkRoQJMGaS4tNSJQ9Sfwr5fWklTR0fiYrc/l7cqkUaqPJCBUgWLnYB6QrKR4kEz2JSLJyvTdWiN6QV5LHZyUmGRDdJrFNtMDj3JW0hJmYQgXmWIDVdLO6+hxMWOOwhPEqYRbVg02eNamEZrSOY2TDePfCTImFhsMSUJt9lQmql4/T3AkjpkdNdu3Csls27yFEo/kzLJTBxygkAYdOYyQK0rCAEYE5vbCKveYLORbAiGWdmiwMbWglu3qOhcDQnLOlYcbXntfz/gdFW3ujCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNyA+PgpzdHJlYW0KeJwzNrRQMIDDFEMuABqUAuwKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDEzMSA+PgpzdHJlYW0KeJxFj8sNBCEMQ+9U4RLyGT6ph9We2P6v6zCaQUL4QSI78TAIrPPyNtDF8NGiwzf+NtWrY5UsH7p6UlYP6ZCHvPIVUGkwUcSFWUwdQ2HOmMrIljK3G+G2TYOsbJVUrYN2PAYPtqdlqwh+qW1h6izxDMJVXrjHDT+QS613vVW+f0JTMJcKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMzOCA+PgpzdHJlYW0KeJw1Ujmu3UAM630KXSCAds2c5wWpfu7fhpRfCkO0VoqajhaVafllIVUtky6/7UltiRvy98kKiROSVyXapQyRUPk8hVS/Z8u8vtacESBLlQqTk5LHJQv+DJfeLhznY2s/jyN3PXpgVYyEEgHLFBOja1k6u8Oajfw8pgE/4hFyrli3HGMVSA26cdoV70PzecgaIGaYlooKXVaJFn5B8aBHrX33WFRYINHtHElwjI1QkYB2gdpIDDmzFruoL/pZlJgJdO2LIu6iwBJJzJxiXTr6Dz50LKi/NuPLr45K+kgra0zad6NJacwik66XRW83b309uEDzLsp/Xs0gQVPWKGl80KqdYyiaGWWFdxyaDDTHHIfMEzyHMxKU9H0ofl9LJrookT8ODaF/Xx6jjJwGbwFz0Z+2igMX8dlhrxxghdLFmuR9QCoTemD6/9f4ef78Axy2gFQKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI0OCA+PgpzdHJlYW0KeJwtUTmSA0EIy+cVekJz0++xy5H3/+kKygGDhkMgOi1xUMZPEJYr3vLIVbTh75kYwXfBod/KdRsWORAVSNIYVE2oXbwevQd2HGYC86Q1LIMZ6wM/Ywo3enF4TMbZ7XUZNQR712tPZlAyKxdxycQFU3XYyJnDT6aMC+1czw3IuRHWZRikm5XGjIQjTSFSSKHqJqkzQZAEo6tRo40cxX7pyyOdYVUjagz7XEvb13MTzho0OxarPDmlR1ecy8nFCysH/bzNwEVUGqs8EBJwv9tD/Zzs5Dfe0rmzxfT4XnOyvDAVWPHmtRuQTbX4Ny/i+D3j6/n8A6ilWxYKZW5kc3RyZWFtCmVuZG9iagozNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDcyID4+CnN0cmVhbQp4nDWMsRHAMAgDe6bQCDZYYO+TS0X2b0N8TgMvHQ+XosFaDbqCI3B1qfzRI125KUWXY86C4XGqX0gxRj2oI+Pex0+5X3AWEn0KZW5kc3RyZWFtCmVuZG9iagozNyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDg4ID4+CnN0cmVhbQp4nDWMuxHAMAhDe0/BCAaLj/fJpUr2b4Pt0ICkd/eCgzpB8rgbKZwubtlXehuPk54mALH0TEPt32BF1YtaFC1fbmPQYiq+P/okIJZ18l449JAS3h/FFx17CmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTAgPj4Kc3RyZWFtCnicNVDLDUMxCLtnChaoFAKBZJ5WvXX/a23QO2ER/0JYyJQIeanJzinpSz46TA+2Lr+xIgutdSXsypognivvoZmysdHY4mBwGiZegBY3YOhpjRo1dOGCpi6VQoHFJfCZfHV76L5PGXhqGXJ2BBFDyWAJaroWTVi0PJ+QTgHi/37D7i3koZLzyp4b+Ruc7fA7s27hJ2p2ItFyFTLUszTHGAgTRR48eUWmcOKz1nfVNBLUZgtOlgGuTj+MDgBgIl5ZgOyuRDlL0o6ln2+8x/cPQABTtAplbmRzdHJlYW0KZW5kb2JqCjE0IDAgb2JqCjw8IC9CYXNlRm9udCAvRGVqYVZ1U2FucyAvQ2hhclByb2NzIDE1IDAgUgovRW5jb2RpbmcgPDwKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ0IC9jb21tYSA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgNTQgL3NpeCAvc2V2ZW4KNTcgL25pbmUgNjEgL2VxdWFsIDgyIC9SIDk3IC9hIDk5IC9jIDEwMSAvZSAxMDUgL2kgMTA3IC9rIC9sIDExNCAvciAxMTYgL3QKMTE4IC92IC93IF0KL1R5cGUgL0VuY29kaW5nID4+Ci9GaXJzdENoYXIgMCAvRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250RGVzY3JpcHRvciAxMyAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2FucwovU3VidHlwZSAvVHlwZTMgL1R5cGUgL0ZvbnQgL1dpZHRocyAxMiAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9Bc2NlbnQgOTI5IC9DYXBIZWlnaHQgMCAvRGVzY2VudCAtMjM2IC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvSXRhbGljQW5nbGUgMAovTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTIgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTUgMCBvYmoKPDwgL1IgMTYgMCBSIC9hIDE3IDAgUiAvYyAxOCAwIFIgL2NvbW1hIDE5IDAgUiAvZSAyMCAwIFIgL2VxdWFsIDIxIDAgUgovaSAyMiAwIFIgL2sgMjMgMCBSIC9sIDI0IDAgUiAvbmluZSAyNiAwIFIgL29uZSAyNyAwIFIgL3BlcmlvZCAyOCAwIFIKL3IgMjkgMCBSIC9zZXZlbiAzMCAwIFIgL3NpeCAzMSAwIFIgL3NwYWNlIDMyIDAgUiAvdCAzMyAwIFIgL3RocmVlIDM0IDAgUgovdHdvIDM1IDAgUiAvdiAzNiAwIFIgL3cgMzcgMCBSIC96ZXJvIDM4IDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTQgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvQ0EgMCAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMiA8PCAvQ0EgMSAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9EZWphVnVTYW5zLW1pbnVzIDI1IDAgUiA+PgplbmRvYmoKMiAwIG9iago8PCAvQ291bnQgMSAvS2lkcyBbIDEwIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKMzkgMCBvYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDIwMDQwODE2MzkxOSswMicwMCcpCi9DcmVhdG9yIChtYXRwbG90bGliIDMuMS4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcpCi9Qcm9kdWNlciAobWF0cGxvdGxpYiBwZGYgYmFja2VuZCAzLjEuMykgPj4KZW5kb2JqCnhyZWYKMCA0MAowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAwOTM4NCAwMDAwMCBuIAowMDAwMDA5MTY1IDAwMDAwIG4gCjAwMDAwMDkxOTcgMDAwMDAgbiAKMDAwMDAwOTI5NiAwMDAwMCBuIAowMDAwMDA5MzE3IDAwMDAwIG4gCjAwMDAwMDkzMzggMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzk4IDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMTk2NSAwMDAwMCBuIAowMDAwMDA3ODMzIDAwMDAwIG4gCjAwMDAwMDc2MzMgMDAwMDAgbiAKMDAwMDAwNzE4MyAwMDAwMCBuIAowMDAwMDA4ODg2IDAwMDAwIG4gCjAwMDAwMDE5ODYgMDAwMDAgbiAKMDAwMDAwMjI4NiAwMDAwMCBuIAowMDAwMDAyNjYzIDAwMDAwIG4gCjAwMDAwMDI5NjYgMDAwMDAgbiAKMDAwMDAwMzEwNCAwMDAwMCBuIAowMDAwMDAzNDIyIDAwMDAwIG4gCjAwMDAwMDM1NjUgMDAwMDAgbiAKMDAwMDAwMzcwNSAwMDAwMCBuIAowMDAwMDAzODU4IDAwMDAwIG4gCjAwMDAwMDM5NzUgMDAwMDAgbiAKMDAwMDAwNDE0NSAwMDAwMCBuIAowMDAwMDA0NTM4IDAwMDAwIG4gCjAwMDAwMDQ2OTAgMDAwMDAgbiAKMDAwMDAwNDgxMSAwMDAwMCBuIAowMDAwMDA1MDQxIDAwMDAwIG4gCjAwMDAwMDUxODEgMDAwMDAgbiAKMDAwMDAwNTU3MSAwMDAwMCBuIAowMDAwMDA1NjYwIDAwMDAwIG4gCjAwMDAwMDU4NjQgMDAwMDAgbiAKMDAwMDAwNjI3NSAwMDAwMCBuIAowMDAwMDA2NTk2IDAwMDAwIG4gCjAwMDAwMDY3NDAgMDAwMDAgbiAKMDAwMDAwNjkwMCAwMDAwMCBuIAowMDAwMDA5NDQ0IDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gMzkgMCBSIC9Sb290IDEgMCBSIC9TaXplIDQwID4+CnN0YXJ0eHJlZgo5NTk4CiUlRU9GCg==\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "w = 69\n",
    "a = 7.3\n",
    "ax.plot(range(-w//2+1, w//2+1), sig.ricker(w, a))\n",
    "ax.grid()\n",
    "ax.axvline(0, color='orange')\n",
    "ax.set_title(f'Ricker wavelet, w={w} a={a}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "r = list(range(60, 120))\n",
    "ax.plot(r, [sum(sig.ricker(w, a)) for w in r])\n",
    "ax.set_yscale('log')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sw = 256\n",
    "w = sig.ricker(sw, a)\n",
    "r = list(range(1, sw//2 - 10))\n",
    "d = [-sum(w[:i]) - sum(w[-i:]) for i in r]\n",
    "ax.plot([sw-2*x for x in r], d)\n",
    "ax.set_yscale('log')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_ser_test(sample_duration=128, nbits=6, signal_amplitude=2.0e-3, decimation=10, threshold_factor=4.0, power_avg_width=2.5, max_lookahead=6.5, pol_score_factor=1.0, seed=0, ax=None, print=print, ser_maxshift=3, debug_range=None):\n",
    "\n",
    "    test_data, signal = generate_test_signal(sample_duration, nbits, signal_amplitude, decimation, seed)\n",
    "    cor_an = correlate(signal, nbits=nbits, decimation=decimation)\n",
    "\n",
    "    power_avg_width = int(power_avg_width * (2**nbits - 1) * decimation)\n",
    "\n",
    "    bit_period = (2**nbits) * decimation\n",
    "    peak_group_threshold = 0.05 * bit_period\n",
    "    hole_patching_threshold = 0.01 * bit_period\n",
    "    \n",
    "    cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n",
    "    if ax:\n",
    "        ax.grid()\n",
    "        ax.plot(cwt_res.T)\n",
    "        \n",
    "    th = np.array([ np.convolve(np.abs(row), np.ones((power_avg_width,))/power_avg_width, mode='same') for row in cwt_res ])\n",
    "\n",
    "    def compare_th(elem):\n",
    "        idx, (th, val) = elem\n",
    "        #print('compare_th:', th.shape, val.shape)\n",
    "        return np.any(np.abs(val) > th*threshold_factor)\n",
    "\n",
    "    peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th.T, cwt_res.T)), compare_th) if val ]\n",
    "    peaks_processed = []\n",
    "    peak_group = []\n",
    "    for group in peaks:\n",
    "        pos = np.mean([idx for idx, _val in group])\n",
    "        #pol = np.mean([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group])\n",
    "        pol = max([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], key=abs)\n",
    "        pol_idx = np.argmax(np.bincount([ np.argmax(np.abs(val)) for _idx, (_th, val) in group ]))\n",
    "        peaks_processed.append((pos, pol, pol_idx))\n",
    "        #print(f'group', pos, pol, pol_idx)\n",
    "        #for pol, (_idx, (_th, val)) in zip([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], group):\n",
    "        #    print('    ', pol, val)\n",
    "        #if ax:\n",
    "        #    ax.axvline(pos, color='cyan', alpha=0.3)\n",
    "        msg = f'peak at {pos} = {pol} idx {pol_idx}: '\n",
    "\n",
    "        if peak_group:\n",
    "            msg += f'continuing previous group: {peak_group[-1]},'\n",
    "            group_start, last_pos, last_pol, peak_pos, last_pol_idx = peak_group[-1]\n",
    "\n",
    "            if abs(pol) > abs(last_pol):\n",
    "                msg += 'larger, '\n",
    "                if ax:\n",
    "                    ax.axvline(pos, color='magenta', alpha=0.5)\n",
    "                peak_group[-1] = (group_start, pos, pol, pos, pol_idx)\n",
    "                \n",
    "            else:\n",
    "                msg += 'smaller, '\n",
    "                if ax:\n",
    "                    ax.axvline(pos, color='blue', alpha=0.5)\n",
    "                peak_group[-1] = (group_start, pos, last_pol, peak_pos, last_pol_idx)\n",
    "        else:\n",
    "            last_pos = None\n",
    "        \n",
    "        if not peak_group or pos - last_pos > peak_group_threshold:\n",
    "            msg += 'terminating, '\n",
    "            if peak_group:\n",
    "                msg += f'previous group: {peak_group[-1]},'\n",
    "                peak_pos = peak_group[-1][3]\n",
    "                if ax:\n",
    "                    ax.axvline(peak_pos, color='red', alpha=0.6)\n",
    "                #ax3.text(peak_pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
    "\n",
    "            msg += f'new group: {(pos, pos, pol, pos, pol_idx)} '\n",
    "            peak_group.append((pos, pos, pol, pos, pol_idx))\n",
    "            if ax:\n",
    "                ax.axvline(pos, color='cyan', alpha=0.5)\n",
    "        \n",
    "        if debug_range:\n",
    "            low, high = debug_range\n",
    "            if low < pos < high:\n",
    "                print(msg)\n",
    "                print(group)\n",
    "\n",
    "    avg_peak = np.mean(np.abs(np.array([last_pol for _1, _2, last_pol, _3, _4 in peak_group])))\n",
    "    print('avg_peak', avg_peak)\n",
    "\n",
    "    noprint = lambda *args, **kwargs: None\n",
    "    def mle_decode(peak_groups, print=print):\n",
    "        peak_groups = [ (pos, pol, idx) for _1, _2, pol, pos, idx in peak_groups ]\n",
    "        candidates = [ (abs(pol)/avg_peak, [(pos, pol, idx)]) for pos, pol, idx in peak_groups if pos < bit_period*2.5 ]\n",
    "\n",
    "        while candidates:\n",
    "            chain_candidates = []\n",
    "            for chain_score, chain in candidates:\n",
    "                pos, ampl, _idx = chain[-1]\n",
    "                score_fun = lambda pos, npos, npol: pol_score_factor*abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
    "                next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
    "\n",
    "                print(f'    candidates for {pos}, {ampl}:')\n",
    "                for score, npos, npol, nidx in next_candidates:\n",
    "                    print(f'        {score:.4f} {npos:.2f} {npol:.2f} {nidx:.2f}')\n",
    "\n",
    "                nch, cor_len = cor_an.shape\n",
    "                if cor_len - pos < 1.5*bit_period or not next_candidates:\n",
    "                    score = sum(score_fun(opos, npos, npol) for (opos, _opol, _oidx), (npos, npol, _nidx) in zip(chain[:-1], chain[1:])) / len(chain)\n",
    "                    yield score, chain\n",
    "\n",
    "                else:\n",
    "                    print('extending')\n",
    "                    for score, npos, npol, nidx in next_candidates[:3]:\n",
    "                        if score > 0.5:\n",
    "                            new_chain_score = chain_score * 0.9 + score * 0.1\n",
    "                            chain_candidates.append((new_chain_score, chain + [(npos, npol, nidx)]))\n",
    "            print('chain candidates:')\n",
    "            for score, chain in sorted(chain_candidates, reverse=True):\n",
    "                print('    ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol, _idx in chain])])\n",
    "            candidates = [ (chain_score, chain) for chain_score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n",
    "\n",
    "    res = sorted(mle_decode(peak_group, print=noprint), reverse=True)\n",
    "    #for i, (score, chain) in enumerate(res):\n",
    "    #    print(f'Chain {i}@{score:.4f}: {chain}')\n",
    "    (_score, chain), *_ = res\n",
    "\n",
    "    def viz(chain, peaks):\n",
    "        last_pos = None\n",
    "        for pos, pol, nidx in chain:\n",
    "            if last_pos:\n",
    "                delta = int(round((pos - last_pos) / bit_period))\n",
    "                if delta > 1:\n",
    "                    print(f'skipped {delta-1} symbols at {pos}/{last_pos}')\n",
    "                    \n",
    "                    # Hole patching routine\n",
    "                    for i in range(1, delta):\n",
    "                        est_pos = last_pos + (pos - last_pos) / delta * i\n",
    "\n",
    "                        icandidates = [ (ipos, ipol, iidx) for ipos, ipol, iidx in peaks if abs(est_pos - ipos) < hole_patching_threshold ]\n",
    "                        if not icandidates:\n",
    "                            yield None\n",
    "                            continue\n",
    "\n",
    "                        ipos, ipol, iidx = max(icandidates, key = lambda e: abs(e[1]))\n",
    "\n",
    "                        decoded = iidx*2 + (0 if ipol < 0 else 1)\n",
    "                        print(f'interpolating, last_pos={last_pos}, delta={delta}, pos={pos}, est={est_pos} dec={decoded}')\n",
    "                        yield decoded\n",
    "            \n",
    "            decoded = nidx*2 + (0 if pol < 0 else 1)\n",
    "            yield decoded\n",
    "            if ax:\n",
    "                ax.axvline(pos, color='blue', alpha=0.5)\n",
    "                ax.text(pos-20, 0.0, f'{decoded}', horizontalalignment='right', verticalalignment='center', color='black')\n",
    "\n",
    "            last_pos = pos\n",
    "\n",
    "    decoded = list(viz(chain, peaks_processed))\n",
    "    print('decoding [ref|dec]:')\n",
    "    match_result = []\n",
    "    for shift in range(-ser_maxshift, ser_maxshift):\n",
    "        msg = f'=== shift = {shift} ===\\n'\n",
    "        failures = -shift if shift < 0 else 0 # we're skipping the first $shift symbols\n",
    "        a = test_data if shift > 0 else test_data[-shift:]\n",
    "        b = decoded if shift < 0 else decoded[shift:]\n",
    "        for i, (ref, found) in enumerate(itertools.zip_longest(a, b)):\n",
    "            if ref is None: # end of signal\n",
    "                break\n",
    "            msg += f'{ref if ref is not None else -1:>3d}|{found if found is not None else -1:>3d} {\"✔\" if ref==found else \"✘\" if found else \" \"}   '\n",
    "            if ref != found:\n",
    "                failures += 1\n",
    "            if i%8 == 7:\n",
    "                msg += '\\n'\n",
    "        match_result.append((failures, msg))\n",
    "    failures, msg = min(match_result, key=lambda e: e[0])\n",
    "    print(msg)\n",
    "    ser = failures/len(test_data)\n",
    "    print(f'Symbol error rate e={ser}: {failures}/{len(test_data)}')\n",
    "    br = sampling_rate / decimation / (2**nbits) * nbits * (1 - ser) * 3600\n",
    "    print(f'maximum bitrate r={br} b/h')\n",
    "    return ser, br"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "853e76b6724f4c0fb44f9e39eef963e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scheduled 50 tasks. waiting...\n",
      "done\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5a641a7b50>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "default_params = dict(\n",
    "        decimation=10,\n",
    "        power_avg_width=2.5,\n",
    "        max_lookahead=6.5)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 9))\n",
    "\n",
    "def calculate_ser(v, seed, nbits, thf, reps, duration):\n",
    "    st = np.random.RandomState(seed)\n",
    "    params = dict(default_params)\n",
    "    params['signal_amplitude'] = v\n",
    "    params['nbits'] = nbits\n",
    "    params['threshold_factor'] = thf\n",
    "    sers, brs = [], []\n",
    "    for i in range(reps):\n",
    "        seed = st.randint(0xffffffff)\n",
    "        try:\n",
    "            ser, br = run_ser_test(**params, sample_duration=duration, print=noprint, seed=seed)\n",
    "            sers.append(ser)\n",
    "            brs.append(br)\n",
    "        except Exception as e:\n",
    "            traceback.print_exc()\n",
    "            print('got', e, 'seed', seed, 'params', params)\n",
    "            #sers.append(1.0)\n",
    "            #brs.append(0.0)\n",
    "        #print(f'nbits={nbits} ampl={v:>.5f} seed={seed:08x} > ser={ser:.5f}')\n",
    "    sers, brs = np.array(sers), np.array(brs)\n",
    "    ser, std = np.mean(sers), np.std(sers)\n",
    "    #print(f'signal_amplitude={v:<.5f}: ser={ser:<.5f} ±{std:<.5f}, br={np.mean(brs):<.5f}')\n",
    "    return ser, std\n",
    "\n",
    "results = {}\n",
    "with tqdm(total = 0) as tq:\n",
    "    with multiprocessing.Pool(multiprocessing.cpu_count()//2) as pool:\n",
    "        for nbits, thf, reps, points, duration in [(5, 4.0, 5, 25, 64), (6, 4.0, 5, 25, 64)]: #[(5, 4.0, 50, 25, 128), (6, 4.0, 25, 25, 64), (7, 5.0, 10, 10, 64), (8, 6.0, 5, 10, 32)]:\n",
    "            #print(f'nbits={nbits}')\n",
    "            st = np.random.RandomState(0)\n",
    "            vs = 0.1e-3 * 10 ** np.linspace(0, 1.5, points)\n",
    "            results[nbits] = [ pool.apply_async(calculate_ser, (v, st.randint(0xffffffff), nbits, thf, reps, duration), callback=lambda _res: tq.update(1)) for v in vs ]\n",
    "            tq.total += len(vs)\n",
    "            tq.refresh()\n",
    "        \n",
    "        pool.close()\n",
    "        pool.join()\n",
    "\n",
    "        print(f'scheduled {tq.total} tasks. waiting...')\n",
    "        results = { nbits: [ res.get() for res in series ] for nbits, series in results.items() }\n",
    "        print('done')\n",
    "\n",
    "with open(f'dsss_experiments_res-{datetime.datetime.now():%Y-%m-%d %H:%M:%S}.json', 'w') as f:\n",
    "    json.dump(results, f)\n",
    "    \n",
    "for nbits, res in results.items():\n",
    "    data = np.array(res)\n",
    "    sers, stds = data[:,0], data[:,1]\n",
    "\n",
    "    l, = ax.plot(vs*1e3, np.clip(sers, 0, 1), label=f'{nbits} bit')\n",
    "    ax.fill_between(vs*1e3, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.3)\n",
    "ax.grid()\n",
    "ax.set_xlabel('Amplitude in mHz')\n",
    "ax.set_ylabel('Symbol error rate')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f59e522d5e0>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 9))\n",
    "\n",
    "#    sers, brs = np.array(sers), np.array(brs)\n",
    "#    ser, std = np.mean(sers), np.std(sers)\n",
    "#    results = { nbits: [ res.get() for res in series ] for nbits, series in results.items() }\n",
    "\n",
    "with open(f'data/dsss_experiments_res-2020-02-19-19-30-05.json', 'r') as f:\n",
    "    results = json.load(f)\n",
    "\n",
    "for nbits, series in results.items():\n",
    "    series = [ [ mean for mean, _std, _msg in reps if mean is not None ] for reps in series ]\n",
    "    sers = np.array([ np.mean(values) for values in series ])\n",
    "    stds = np.array([ np.std(values)  for values in series ])\n",
    "\n",
    "    # FIXME HACK HACK HACK\n",
    "    vs = 0.1e-3 * 10 ** np.linspace(0, 1.5, 25)\n",
    "    \n",
    "    l, = ax.plot(vs, np.clip(sers, 0, 1), label=f'{nbits} bit')\n",
    "    ax.fill_between(vs, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.3)\n",
    "ax.grid()\n",
    "ax.set_xlabel('Amplitude in mHz')\n",
    "ax.set_ylabel('Symbol error rate')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def thf_dependence_plot(plot_nbits=6, ax=None, cbar_ax=None, intercept_ax=None, xlabel=True, ylabel=True, y2label=True):\n",
    "    results = []\n",
    "    for fn in [\n",
    "    #    'data/dsss_experiments_res-2020-02-20-12-18-35.json',\n",
    "    #    'data/dsss_experiments_res-2020-02-20-12-26-07.json',\n",
    "    #    'data/dsss_experiments_res-2020-02-20-12-29-02.json'\n",
    "        'data/dsss_experiments_res-par107-run115-0-2020-04-07-11-41-37.json',\n",
    "        'data/dsss_experiments_res-par107-run115-1-2020-04-07-13-23-42.json',\n",
    "        'data/dsss_experiments_res-par107-run115-2-2020-04-07-08-57-38.json',\n",
    "        'data/dsss_experiments_res-par107-run115-3-2020-04-07-15-48-04.json',\n",
    "    ]:\n",
    "        with open(fn, 'r') as f:\n",
    "            results += json.load(f)\n",
    "\n",
    "    thfs = [thf for (_nbits, thf, _reps, _points, _duration, _decimation), series in results]\n",
    "    cmap = matplotlib.cm.viridis\n",
    "    cm_func = lambda x: cmap((x - min(thfs)) / (max(thfs) - min(thfs)))\n",
    "\n",
    "    thf_sers = {}\n",
    "    for (nbits, thf, reps, points, duration, decimation), series in results:\n",
    "        if nbits != plot_nbits:\n",
    "            continue\n",
    "        data = [ [ mean for mean, _std, _msg in reps if mean is not None ] for _amp, reps in series ]\n",
    "        amps = [ amp*1000 for amp, _reps in series ]\n",
    "        sers = np.array([ np.mean(values) for values in data ])\n",
    "        stds = np.array([ np.std(values)  for values in data ])\n",
    "        thf_sers[thf] = list(zip(amps, sers, stds))\n",
    "\n",
    "        if ax:\n",
    "            l, = ax.plot(amps, np.clip(sers, 0, 1), label=f'thf={thf}', color=cm_func(thf))\n",
    "            ax.fill_between(amps, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.2)\n",
    "            ax.axhline(0.5, color='gray', ls=(0, (3, 4)), lw=0.8)\n",
    "    if ax:\n",
    "        ax.grid()\n",
    "        ax.set_title(f'{plot_nbits}-bit Gold code')\n",
    "        if xlabel:\n",
    "            ax.set_xlabel('Amplitude [mHz]')\n",
    "        if ylabel:\n",
    "            ax.set_ylabel('Symbol Error Rate')\n",
    "\n",
    "    def plot_base_amp(ax):\n",
    "        base_sers = {}\n",
    "        for thf, sers in thf_sers.items():\n",
    "            base = np.mean([ser for amp, ser, std in sorted(sers)[-2:]])\n",
    "            base_std = np.sqrt(np.mean([std**2 for amp, ser, std in sorted(sers)[-2:]]))\n",
    "            base_sers[thf] = (base, base_std)\n",
    "\n",
    "        x = sorted(base_sers.keys())\n",
    "        y = np.array([ base_sers[thf][0] for thf in x ])\n",
    "        std = np.array([ base_sers[thf][1] for thf in x ])\n",
    "        l = ax.plot(x, y, label='SER at large amplitudes')\n",
    "        ax.fill_between(x, y-std, y+std, color=l[0].get_color(), alpha=0.3)\n",
    "        return l\n",
    "\n",
    "    def plot_intercepts(ax, SER_TH = 0.5):\n",
    "        intercepts = {}\n",
    "        for thf, sers in thf_sers.items():\n",
    "            last_ser, last_amp, last_std = 0, 0, 0\n",
    "            for amp, ser, std in sorted(sers):\n",
    "                if last_ser > SER_TH and ser < SER_TH:\n",
    "                    icp = last_amp + (SER_TH - last_ser) / (ser - last_ser) * (amp - last_amp)\n",
    "                    ic_std = abs(last_amp - amp) / 2# np.sqrt(np.mean(last_std**2 + std**2))\n",
    "                    intercepts[thf] = (icp, ic_std)\n",
    "                    break\n",
    "                last_amp, last_ser = amp, ser\n",
    "            else:\n",
    "                intercepts[thf] = None, None\n",
    "\n",
    "        ser_valid = [thf for thf, (ser, _std) in intercepts.items() if ser is not None]\n",
    "        #ax.axvline(min(ser_valid), color='red')\n",
    "        #ax.axvline(max(ser_valid), color='red')\n",
    "\n",
    "        x = sorted(intercepts.keys())\n",
    "        data = np.array([ intercepts[thf] for thf in x ])\n",
    "        y = data[:,0]\n",
    "        std = data[:,1]\n",
    "\n",
    "        if ax:\n",
    "            ax.set_xlim([min(x), max(x)])\n",
    "            l = ax.plot(x, y, label='Amplitude at SER=0.5', color='orange')\n",
    "        else:\n",
    "            l = None\n",
    "\n",
    "        x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n",
    "        y, std = np.array(y), np.array(std)\n",
    "        if ax:\n",
    "            ax.fill_between(x, y-std, y+std, color=l[0].get_color(), alpha=0.3)\n",
    "\n",
    "            trans = matplotlib.transforms.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "            ax.fill_between([-1, min(ser_valid)], 0, 1, facecolor='red', alpha=0.2, transform=trans, zorder=1)\n",
    "            ax.fill_between([max(ser_valid), max(ser_valid)*10], 0, 1, facecolor='red', alpha=0.2, transform=trans)\n",
    "            ax.set_ylim([min(y)*0.9, max(y)*1.1])\n",
    "            ax.grid()\n",
    "        \n",
    "        best_ampl = (np.inf, np.nan)\n",
    "        for yval, stdval in zip(y, std):\n",
    "            if yval < best_ampl[0]:\n",
    "                best_ampl = [yval, stdval]\n",
    "        \n",
    "        return l, best_ampl\n",
    "\n",
    "    l1, best_ampl = plot_intercepts(intercept_ax)\n",
    "    if intercept_ax:\n",
    "        intercept_ax_twin = intercept_ax.twinx()\n",
    "        l2 = plot_base_amp(intercept_ax_twin)\n",
    "        \n",
    "        intercept_ax.set_title(f'{plot_nbits}-bit Gold code')\n",
    "        if xlabel:\n",
    "            intercept_ax.set_xlabel('Threshold factor')\n",
    "        if ylabel:\n",
    "            intercept_ax.set_ylabel('Amplitude [mHz]')\n",
    "        intercept_ax.set_ylim([-0.5, 5])\n",
    "        intercept_ax_twin.set_ylim([-0.1, 1])\n",
    "        if y2label:\n",
    "            intercept_ax_twin.set_ylabel('Symbol Error Rate')\n",
    "        else:\n",
    "            intercept_ax_twin.set_yticklabels([])\n",
    "        intercept_ax.legend(l1 + l2, [l.get_label() for l in l1+l2], loc=4)\n",
    "\n",
    "    if cbar_ax:\n",
    "        norm = matplotlib.colors.Normalize(vmin=min(thfs), vmax=max(thfs))\n",
    "        cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', label=\"Threshold factor\")\n",
    "        \n",
    "    return best_ampl\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "def plot_gold_sensitivity(nbitses=[5,6,7,8]):\n",
    "    nbitses = np.array(nbitses)\n",
    "    ampls = np.array([ thf_dependence_plot(dep_n) for dep_n in nbitses ])\n",
    "    fig, ax = plt.subplots()\n",
    "    l = ax.plot(nbitses, ampls[:,0])\n",
    "    ax.fill_between(nbitses, ampls[:,0]-ampls[:,1], ampls[:,0]+ampls[:,1], color=l[0].get_color(), alpha=0.3)\n",
    "    ax.grid()\n",
    "    ax.set_xlabel('Gold code bits')\n",
    "    ax.set_ylabel('Amplitude at SER=0.5 [mHz]')\n",
    "    ax.set_ylim([0, 2])\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
    "    return fig\n",
    "\n",
    "def plot_amplitude_ber():\n",
    "    fig = plt.figure(figsize=(12, 9))\n",
    "    gs = plt.GridSpec(2, 3, figure=fig, width_ratios=[1, 1, 0.05])\n",
    "    \n",
    "    cbar_ax = fig.add_subplot(gs[0, 2])\n",
    "    \n",
    "    axs = np.empty([2, 2], dtype=object)\n",
    "    for nbits, row, col, cbar_ax in [\n",
    "        (5, 0, 0, cbar_ax),\n",
    "        (6, 0, 1, None),\n",
    "        (7, 1, 0, None),\n",
    "        (8, 1, 1, None)\n",
    "    ]:\n",
    "        ax = axs[row, col] = fig.add_subplot(gs[row, col])\n",
    "        if row == 1:\n",
    "            ax.get_shared_x_axes().join(axs[0, col])\n",
    "        if col == 1:\n",
    "            ax.get_shared_y_axes().join(axs[row, 0])\n",
    "        thf_dependence_plot(nbits, ax=ax, cbar_ax=cbar_ax, xlabel=row==1, ylabel=col==0)\n",
    "    return fig\n",
    "\n",
    "def plot_thf_graph():\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(12, 9), sharex='col', sharey='row', gridspec_kw={'wspace': 0.1})\n",
    "    for nbits, ax in zip([5,6,7,8], axs.flatten()):\n",
    "        thf_dependence_plot(nbits, intercept_ax=ax, xlabel=nbits in [7, 8], ylabel=nbits in [5, 7], y2label=nbits in [6, 8])\n",
    "\n",
    "plot_gold_sensitivity();\n",
    "plot_amplitude_ber();\n",
    "plot_thf_graph();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig.savefig('dsss_prototype_symbol_error_rate_5-8_bit.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-83-27310fbba84b>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e988aebea1bc441484ddc3d63d07f9c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "<ipython-input-83-27310fbba84b>:24: RuntimeWarning: divide by zero encountered in log10\n",
      "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
      "<ipython-input-83-27310fbba84b>:82: UserWarning: Attempting to set identical left == right == 10 results in singular transformations; automatically expanding.\n",
      "  ax.set_xlim([min(x), max(x)])\n"
     ]
    }
   ],
   "source": [
    "fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\n",
    "empty.axis('off')\n",
    "#fig.tight_layout()\n",
    "\n",
    "results = []\n",
    "\n",
    "for fn in [\n",
    "#    'data/dsss_experiments_res-2020-02-20-14-10-13.json',\n",
    "#    'data/dsss_experiments_res-2020-02-20-13-21-57.json',\n",
    "#    'data/dsss_experiments_res-2020-02-20-13-23-47.json',\n",
    "#    'data/dsss_experiments_res-2020-02-20-19-51-21.json',\n",
    "#    'data/dsss_experiments_res-2020-02-20-20-43-32.json',\n",
    "#    'data/dsss_experiments_res-2020-02-20-21-36-42.json',\n",
    "    'data/dsss_experiments_res-par107-run115-0-2020-04-07-11-41-37.json',\n",
    "    'data/dsss_experiments_res-par107-run115-1-2020-04-07-13-23-42.json',\n",
    "    'data/dsss_experiments_res-par107-run115-2-2020-04-07-08-57-38.json',\n",
    "    'data/dsss_experiments_res-par107-run115-3-2020-04-07-15-48-04.json',\n",
    "]:\n",
    "    with open(fn, 'r') as f:\n",
    "        results += json.load(f)\n",
    "\n",
    "decimations = [decimation for (_nbits, thf, _reps, _points, _duration, decimation), series in results if decimation > 0]\n",
    "cmap = matplotlib.cm.viridis\n",
    "cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n",
    "\n",
    "decimation_sers = {}\n",
    "for (nbits, thf, reps, points, duration, decimation), series in results:\n",
    "    if not decimation > 0:\n",
    "        continue\n",
    "    data = [ [ mean for mean, _std, _msg in reps if mean is not None ] for _amp, reps in series ]\n",
    "    amps = [ amp for amp, _reps in series ]\n",
    "    sers = np.array([ np.mean(values) for values in data ])\n",
    "    stds = np.array([ np.std(values)  for values in data ])\n",
    "    decimation_sers[decimation] = list(zip(amps, sers, stds))\n",
    "    \n",
    "    amps = [ amp*1000 for amp in amps ]\n",
    "    l, = ax.plot(amps, np.clip(sers, 0, 1), label=f'decimation={decimation}', color=cm_func(decimation))\n",
    "    ax.fill_between(amps, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.2)\n",
    "    ax.axhline(0.5, color='gray', ls=(0, (3, 4)), lw=0.8)\n",
    "ax.grid()\n",
    "ax.set_xlabel('Amplitude [mHz]')\n",
    "ax.set_ylabel('Symbol error rate')\n",
    "\n",
    "norm = matplotlib.colors.Normalize(vmin=np.log10(min(decimations)), vmax=np.log10(max(decimations)))\n",
    "tick_decs = sorted(decimations)\n",
    "tick_decs = tick_decs[:4] + tick_decs[5::5]\n",
    "yticks = [np.log10(d) for d in tick_decs]\n",
    "cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', ticks=yticks)\n",
    "cb1t = cbar_ax.twinx()\n",
    "cb1t.set_ylim(cbar_ax.get_ylim())\n",
    "cb1t.set_yticks(yticks)\n",
    "\n",
    "cbar_ax.set_yticklabels([f'{d/sampling_rate:.1f}' for d in tick_decs])\n",
    "cbar_ax.set_ylabel(\"chip duration [s]\", labelpad=-70)\n",
    "\n",
    "cb1t.set_yticklabels([f'{d/sampling_rate * 2**nbits:.1f}' for d in tick_decs])\n",
    "cb1t.set_ylabel(\"symbol duration [s]\")\n",
    "\n",
    "\n",
    "def plot_intercepts(ax, SER_TH = 0.5):\n",
    "    intercepts = {}\n",
    "    for dec, sers in decimation_sers.items():\n",
    "        last_ser, last_amp, last_std = 0, 0, 0\n",
    "        for amp, ser, std in sorted(sers):\n",
    "            if last_ser > SER_TH and ser < SER_TH:\n",
    "                icp = last_amp + (SER_TH - last_ser) / (ser - last_ser) * (amp - last_amp)\n",
    "                ic_std = (abs(last_amp - amp) / 2) + np.sqrt(np.mean(last_std**2 + std**2))\n",
    "                intercepts[dec] = (icp, ic_std)\n",
    "                break\n",
    "            last_amp, last_ser = amp, ser\n",
    "        else:\n",
    "            intercepts[dec] = None, None\n",
    "    \n",
    "    ser_valid = [dec for dec, (ser, _std) in intercepts.items() if ser is not None]\n",
    "    #ax.axvline(min(ser_valid), color='red')\n",
    "    #ax.axvline(max(ser_valid), color='red')\n",
    "    \n",
    "    x = sorted(intercepts.keys())\n",
    "    data = np.array([ intercepts[dec] for dec in x ])\n",
    "    y = data[:,0]\n",
    "    std = data[:,1]\n",
    "    ax.set_xlim([min(x), max(x)])\n",
    "    y = [ v*1000 if v is not None else v for v in y ]\n",
    "    l = ax.plot(x, y, label='Amplitude at SER=0.5 [mHz]', color='orange')\n",
    "    #ax.legend(loc=3)\n",
    "    ax.set_ylabel('Amplitude at SER=0.5 [mHz]')\n",
    "    ax.grid()\n",
    "    \n",
    "    x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n",
    "    y, std = np.array(y), np.array(std)\n",
    "    ax.fill_between(x, y-std, y+std, color=l[0].get_color(), alpha=0.3)\n",
    "    \n",
    "    trans = matplotlib.transforms.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "    ax.fill_between([-1, min(ser_valid)], 0, 1, facecolor='red', alpha=0.2, transform=trans, zorder=1)\n",
    "    ax.fill_between([max(ser_valid), max(ser_valid)*10], 0, 1, facecolor='red', alpha=0.2, transform=trans)\n",
    "    ax.set_ylim([min(y)*0.9, max(y)*1.1])\n",
    "    ax.set_xscale('log')\n",
    "    ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, _: '{:g}'.format(x)))\n",
    "    xticks = [1, 2, 5, 10, 20, 50]\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels([ f'{x/sampling_rate:.1f}' for x in xticks ])\n",
    "    ax.set_xlim([1, 60])\n",
    "    ax.set_xlabel('chip duration [s]')\n",
    "    \n",
    "    axt = ax.twiny()\n",
    "    axt.set_xlim(ax.get_xlim())\n",
    "    axt.set_xscale('log')\n",
    "    axt.set_xticks(xticks)\n",
    "    axt.set_xticklabels([ f'{x/sampling_rate * 2**nbits:.1f}' for x in xticks ])\n",
    "    axt.set_xlabel('symbol duration [s]')\n",
    "    \n",
    "    return l\n",
    "\n",
    "l1 = plot_intercepts(intercept_ax)"
   ]
  }
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